MasterCAM中MFC对话框的应用

为在CAM软件MasterCAM中使用MFC对话框,对其进行了二次开发,用动态链接库来实现MasterCAM和MFC对话框之间的通信,并在MasterCAM二次开发环境C-Hooks的应用程序中调用MFC对话框资源,给出了接口模型、实现过程和实例,实现了MFC对话框在MasterCAM中的应用.     

Ni3Al合金中晶界与位错交互作用的研究

运用嵌入原子法势函数和静态驰豫最陡梯度的计算方法,原子模拟了Ni_3Al合金中晶界与位错的交互作用.主要研究了晶界与位错交互作用的能量特征、晶界结构单元的畸变行为和晶界附近位错的核心结构,并分析讨论了各种因素对它们的影响以及与Ni_3Al晶界韧脆行为之间的关系     

生物质秸秆墙材—新型墙材的发展方向

秸秆是第二大“森林”资源，将秸秆制备成墙材，既找到大量秸秆资源化的出路，也填补了墙材即将面临短缺的难题。综述了生物质秸秆墙材的研究进展，讨论了目前秸秆墙材研究与应用中存在的问题，探讨了秸秆墙材将来研究的趋势。     

聚丙烯耐熔垂性能的改进

本文了共聚、共混、填充等技术改进聚丙烯耐熔垂性能的规律，并对改性后聚丙烯的力学性能进行了研究。     

一种检测单晶炉热场温度的新方法

为去除单晶炉热场温度测量信号中的低频干扰、提高温度信号的估计精度,本文提出了一种基于跨维模拟退火(trans dimensional simulated annealing,TDSA)的单晶炉热场温度检测方法.该方法首先将AIC(Akaike information criterion)信号个数判断准则纳入采样机制中以便对干扰个数采样估计,进而利用所设计的基于信号频谱和轮盘赌思想的Metropolis Hastings机制对相应个数的干扰频率进行采样,最后设计了混合均匀和高斯采样机制对单晶炉热场温度进行采样估计.仿真和单晶炉工程实验结果表明,该方法在干扰个数未知的情况下,能够有效地抑制低频干扰、准确检测单晶炉热场的温度.     

磷酸铁锂锂离子电池正极材料的研究

锂离子电池是绿色高能可充电池，具有工作电压高、比能量大、自放电小、循环寿命长、无记忆效应、无环境污染等突出优点。本文从磷酸铁锂的结构与性能、材料的制备方法、改性、粒径控制等几方面综述了近年来对橄榄石型磷酸铁锂（LiFePO4）锂离子电池正极材料的研究进展。材料的粒度大小及其分布、离子和电子的传导能力对产品的电化学性能影响很大。在制备时，采用惰性气氛、掺杂导电材料和控制晶粒生长制备粉体是获得性能优良的LiFePO4的有效方法。     

S＆W超选择性裂解炉分区裂解探讨

介绍了S＆W超选择性裂解炉分区裂解改造的情况，包括分区裂解炉的运行情况、对后系统的影响、对产品收率的影响、投用过程存在的问题及处理。指出分区裂解能提高装置的生产能力，而产品的收率基本不变。     

分流叶片对离心泵内部固液两相流的影响

为了研究分流叶片对离心泵内部固液两相流的影响,建立固液两相流模型,对无分流叶片离心泵和3种带分流叶片的离心泵进行全流道非定常数值模拟。重点分析了添加分流叶片对离心泵外特性、叶片表面压力脉动、叶片表面颗粒体积分数分布、叶片表面颗粒滑移速度的影响。分析结果表明:添加分流叶片以后,离心泵的扬程和效率均有显著的提高,叶片表面的颗粒分布更加均匀,叶片表面颗粒的滑移速度有不同程度的下降,使颗粒对叶片表面的磨损程度得以降低;当分流叶片的进口直径为0.65D2时,叶片出水边处的压力脉动幅值最小,减小了离心泵在运行过程中的振动和噪声。     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts

The Yellow River Estuary area of China is under great pressure from both human intervention and natural processes. For analysis of the changes in this area, this article presents a novel change-detection method based on a local fit-search model and kernel-induced graph cuts in multitemporal synthetic aperture radar images. Change detection involves assigning a label to every pixel. This task is naturally formulated in terms of energy minimization, which can be effectively solved by graph cuts. The difference image is transformed implicitly by a kernel function so that an alternative to complex modelling of the original data makes the piecewise constant model become applicable for graph cuts formulation. An issue is that graph cuts are sensitive to the initial estimate. The local fit-search model is proposed to approximate to the local histogram while selecting an optimal threshold for the initial labelling, which leads to an effective constraint for graph cuts and computational benefits as well. Visual and quantitative analyses obtained on the Yellow River Estuary data set confirm the effectiveness of the proposed method and that it outperforms the other state-of-the-art methods of change detection.